Abstract
Distinguishing patients with monogenic diabetes from Type 1 diabetes (T1D) is important for correct diagnosis, treatment and to select patients for gene discovery studies. We assessed whether a T1D genetic risk score (T1D-GRS) generated from T1D-associated common genetic variants provides a novel way to discriminate monogenic diabetes from T1D. The T1D-GRS was highly discriminative of proven MODY (n=805) and T1D (n=1963) (ROC-AUC=0.87). A T1D-GRS of >0.280 (>50th T1D centile) was indicative of T1D (94% specificity, 50% sensitivity). We then analyzed the T1D-GRS in 242 White-European patients with neonatal diabetes (NDM) who had been tested for all known neonatal diabetes genes. Monogenic NDM was confirmed in 90%, 59% and 8% in patients with GRS <5th T1D centile, 50-75th T1D centile and >75th T1D centile, respectively. Applying a GRS 50th T1D centile cut-off in 48 NDM patients with no known genetic cause, identified those most likely to have a novel monogenic etiology by highlighting patients with probable early-onset T1D (GRS >50th T1D centile) who were diagnosed later, had less syndromic presentation but had additional autoimmune features compared to proven monogenic NDM. The T1D-GRS is a novel tool to improve the use of biomarkers in the discrimination of monogenic diabetes from T1D.
Introduction
Distinguishing patients with monogenic diabetes from the more common Type 1 diabetes (T1D) has important clinical and scientific implications. Many subtypes of monogenic diabetes can be treated with sulphonylurea tablets, either low dose (HNF1A/HNF4A) (1) or high dose (KCNJ11/ABCC8) (2), while patients with T1D are treated with lifelong insulin injections. Identifying new genetic causes of diabetes can lead to discovery of biologically important pathways and may provide potential therapeutic targets. The genetic etiology is unknown in approximately 20% of patients with MODY (maturity-onset diabetes of young) and neonatal diabetes (NDM) (3). Therefore, novel tools that facilitate the discovery of new genetic causes of diabetes are useful, particularly ones that discriminate monogenic diabetes from T1D.
T1D is a polygenic disease that shows well-characterised strong genetic predisposition from HLA and non-HLA loci (4). Large-scale genome-wide association studies have identified common genetic variants (single nucleotide polymorphisms (SNPs)) in HLA as well as more than 40 non-HLA genes that contribute to T1D genetic susceptibility (4–6). Genotyping these variants and combining the risk of each inherited allele to create a Type 1 diabetes genetic risk score (T1D-GRS) allows us to accurately capture an individual’s polygenic susceptibility to T1D (7; 8). The T1D-GRS has been shown to predict development of T1D and islet autoantibodies (7; 9) and to predict the development of insulin deficiency in patients with diabetes diagnosed between 20-40 years (8).
In this study, we assessed whether a T1D-GRS generated from 30 common T1D associated SNPs can provide a novel way to discriminate monogenic diabetes from T1D. We first assessed the ability of the genetic risk score to discriminate T1D and confirmed MODY. We then assessed its utility in patients with neonatal diabetes diagnosed under 6 months of age to identify i) patients with a known monogenic etiology and ii) patients with a high likelihood of a novel monogenic etiology.
Research Design and Methods
Study populations
i). Patients with Type 1 Diabetes
T1D patients were from the Wellcome Trust Case Control Consortium (WTCCC) and have been previously described in detail (10). In brief, the WTCCC T1D cases (n=1963) were all clinically diagnosed with T1D under 17 years of age and were insulin treated from diagnosis. Patients with known MODY or neonatal diabetes patients were excluded.
ii). Patients with confirmed MODY
We included 805 White-European MODY patients with a confirmed monogenic etiology on genetic testing (415 patients with HNF1A MODY, 346 with GCK MODY, 42 with HNF4A MODY and 2 with HNF1B MODY). The median age of diagnosis was 20 years (interquartile range 15-30) and 532 patients were female.
iii). Patients with neonatal diabetes
We included 242 White-European patients with neonatal diabetes diagnosed before 6 months of age. They had undergone comprehensive genetic testing of all 23 known neonatal diabetes genes using a targeted next-generation sequencing panel as previously described (3; 11). A monogenic etiology was confirmed in 80% (n=194) of patients: comprising mutations in KCNJ11 (n=79), ABCC8 (n=35), INS (n=30), 6q24 methylation defect (n=23), GATA6 (n=15), EIF2AK3 (n=5), FOXP3 (n=4) and one mutation each in GATA4, GCK and PTF1A. This comprehensive genetic testing did not identify a mutation in the remaining 20% of cases (n=48).
Generating T1D-GRS in WTCCC, MODY and neonatal diabetes patients
The T1D-GRS was generated using 30 SNPs as previously described (8). We excluded samples where genotyping results were missing for any of the alleles that had the greatest influence on the genetic risk score (DR3/DR4-DQ8 or HLA_DRB1_15) or >2 other SNPs.
Statistical analysis
The appropriate parametric (t-test) and nonparametric tests (Mann Whitney and Kruskal-Wallis) were used to compare the continuous variables. Fisher’s exact test was used to compare categorical variables. Logistic regression and receiver operative curve analysis were used to measure the discriminatory power of the T1D-GRS. Statistical analysis was carried out using STATA 13.1(StataCorp LP, Texas, USA).
Ethical approval
The study was approved by the Genetics of Beta Cell Research Bank, Exeter, UK with ethical approval from the North Wales Research Ethics Committee, UK.
Results
T1D-GRS was highly discriminatory of MODY and T1D
T1D-GRS of the MODY cohort was lower (mean GRS 0.231, 95% CI 0.228-0.233) compared to the WTCCC-T1D cohort (0.279, 95% CI 0.278-0.280, p=4×10-136) but was similar to the WTCCC controls (0.229, 95% CI, 0.228-0.230, p=0.26) (Fig 1A). The analysis of the T1D-GRS using the receiver operating characteristic (ROC) curve showed that the T1D-GRS was highly discriminatory between MODY and T1D (ROC-AUC 0.87, 95% CI 0.86-0.89) (Fig 1B).
Figure 1. T1D genetic risk score is higher in T1D than in confirmed MODY and non-diabetic controls.
A) Dot-plot of T1D genetic risk score from 1963 T1D patients, 805 MODY (subtype of monogenic diabetes) and 2938 non-diabetic controls. Blue bar highlights the median T1D genetic risk score for each group. Upper red horizontal line shows genetic risk score equivalent to 50th centile for T1D cohort and lower red horizontal line shows genetic risk score equivalent to 5th centile for T1D cohort. The control and T1D patients came from WTCCC (10). B) ROC curve (AUC=0.87) for T1D genetic risk score to discriminate MODY (n=805) and T1D (n=1963).
High specificity of T1D-GRS cut-offs based on T1D centiles
To further understand the discriminatory power of the T1D-GRS, MODY and T1D patients were categorized using a T1D-GRS score equivalent to the 5th, 25th, 50th and 75th centile of the T1D cohort (Table 1). Only 6% of the MODY patients had T1D-GRS >0.280 (>50th T1D centile) compared to 50% of T1D patients thus this level of GRS was indicative of T1D with 94% specificity and 50% sensitivity (Table 1). Conversely, 53% of the MODY patients had T1D-GRS <0.234 (<5th T1D centile) compared to only 5% of the T1D patients providing 50% sensitivity and 95% specificity to identify MODY (Table 1).
Table 1.
T1D genetic risk score is discriminatory for T1D and MODY. The groups were categorized by genetic risk score centiles of the T1D cohort.
| Categories based on genetic risk score centiles of T1D cohort (acutal score value) | T1D n (%) | Confirmed MODY n (%) | Total | Odds Ratio for T1D (95% CI) |
|---|---|---|---|---|
| <5th centile (<0.234) | 98 (5) | 423 (53) | 521 | 1 |
| 5-25th centile (0.234-0.262) | 392(20) | 242 (30) | 634 | 7 (5-9) |
| 26-50th centile (0.263-0.280) | 492(25) | 89 (11) | 581 | 24 (16-36) |
| 51-75th centile (0.281-0.299) | 491(25) | 42 (5) | 533 | 50 (29-89) |
| >75th centile (>0.299) | 490 (25) | 9 (1) | 499 | 235 (73-755) |
| 1963 (100) | 805 (100) | 2768 |
T1D-GRS can identify patients with neonatal diabetes who had monogenic etiology on comprehensive genetic testing
To further assess the utility of the T1D-GRS, we compared the result of comprehensive genetic testing for neonatal diabetes with different values of T1D-GRS. In the 242 patients with neonatal diabetes, comprehensive genetic testing showed 81% (194/242) had monogenic NDM. Out of 117 patients with T1D-GRS <5th T1D centile, 90% (105/117) were shown to have monogenic NDM (Fig 2A). The proportions of proven monogenic NDM was reduced to 59% (13/22) for patients with T1D-GRS of 50-75th T1D centile, and only 8% (1/13) in patients with T1D-GRS >75th T1D centile (p=1.5×10-11)(Fig 2A).
Figure 2. T1D genetic risk score identified proven and probable monogenic neonatal diabetes.
A) T1D genetic risk score identified proven monogenic neonatal diabetes. 242 White-European patients with neonatal diabetes (diagnosed before 6 months of age) who had comprehensive genetic testing for all 23 neonatal diabetes genes were grouped by their T1D-GRS (11). The subgroups of T1D-GRS are based on centiles of T1D cohort. The black bars show the percentage of monogenic diabetes with a positive genetic test, (proven monogenic NDM, n=194) from low to high score groups (left to right). The white bar represents the percentage of patients with negative genetic test thus the etiology of their diabetes is unknown (NDMX, n=48). Total number of patients is also shown for each group.
B) T1D genetic risk score identified probable monogenic neonatal diabetes and probable T1D in NDMX. The T1D-GRS distribution of NDM patients with unknown etiology (negative genetic test, n=48) showed bimodal distribution with peaks at <5th T1D centile and >75th T1D centile with the nadir around 50th T1D centile. T1D-GRS 50th T1D centile categorized this group as probable T1D (T1D-GRS >50th T1D centile, n=21) and probable monogenic diabetes due to mutations in as yet undiscovered disease genes (T1D-GRS ≤ 50th T1D centile, n=27).
NDM with unknown etiology group showed bimodal distribution of T1D genetic risk score
We next assessed the utility of T1D-GRS to indicate the etiology of diabetes in the 48 patients in whom comprehensive genetic testing was negative (NDMX, unknown etiology). The T1D-GRS in this group (mean T1D-GRS 0.260, 95% CI 0.248-0.275) was intermediate between proven monogenic NDM (0.228, 95% CI 0.222-0.233) and T1D (0.279, 95% CI, 0.278-0.280). There was a bimodal distribution of T1D-GRS in NDMX with peaks at <5th T1D centile and >75th T1D centile with a nadir close to T1D-GRS 0.280 (50th T1D centile) (Fig 2B). This suggests that the NDMX included patients with monogenic NDM due to mutations in as yet undiscovered disease genes as well as T1D.
T1D-GRS identified patients with probable monogenic NDM and early-onset T1D
Dividing the NDMX patients by T1D-GRS 50th T1D centile (the nadir), identified 21/48 (44%) patients with a high genetic predisposition of T1D (T1D-GRS >50th T1D centile). This along with a negative genetic test suggested that these patients might have very early-onset T1D (probable T1D). In contrast, 27/48 (56%) patients with low T1D-GRS ≤ 50th T1D centile were likely to have monogenic NDM due to a novel genetic cause (probable monogenic NDM) (Fig 2B).
These T1D-GRS based etiological groups of NDMX were further supported by their clinical characteristics. The patients with probable T1D presented later (12 weeks vs 1 week, p=0.04), had a higher proportion of other autoimmune disease (14% vs 1% p=0.003) and fewer syndromic presentations (10% vs 40%, p=0.007) than patients with proven monogenic NDM. In contrast, the clinical characteristics of patients with probable monogenic NDM were similar (p>0.05) to patients with proven monogenic NDM (Table 2).
Table 2. Clinical characteristics of patients with proven and probable monogenic neonatal diabetes and probable T1D.
Neonatal diabetes patients with unknown etiology (NDMX) were categorized into probable T1D and probable monogenic neonatal diabetes by T1D genetic risk score equivalent to 50th centile of T1D cohort. Fisher’s exact test was used to compare proportions and Mann-Whitney test was used to compare continuous variables.
| Proven Monogenic NDM<6m | Probable Monogenic NDM< 6 m (T1D-GRS≤50th T1D centile) | Probable T1D< 6 months (T1D-GRS>50th T1D centile) | P value (proven monogenic vs probable monogenic) | P value (proven monogenic vs probable T1D) | |
|---|---|---|---|---|---|
| n=194 | n=27 | n=21 | |||
| T1D-GRS, Median (IQR) | 0.231 (0.206-0.251) | 0.237 (0.210-0.250) | 0.299 (0.295-0.313) | 0.86 | <0.001 |
| Age at diagnosis in weeks, median (IQR) | 5 (1-12) | 1 (0.6-6) | 12 (3-20) | 0.13 | 0.04 |
| Male, n (%) | 111 (57) | 17 (63) | 15 (71) | 0.68 | 0.25 |
| Presence of other autoimmune disorders, n (%) | 1 (1) | 1 (4) | 3 (14) | 0.23 | 0.003 |
| Syndromic presentation, n (%) | 77 (40) | 9 (33) | 2 (10) | 0.67 | 0.007 |
| Consanguineous parents, n (%) | 3 (2) | 0 | 0 | 1.00 | 1.00 |
Discussion
We describe a novel application of polygenic T1D genetic susceptibility to discriminate monogenic diabetes from Type 1 diabetes. We have shown that a T1D-GRS aids the discrimination of MODY and T1D in large White-European cohorts. Using this tool in patients with neonatal diabetes revealed a lower T1D-GRS in those with a genetic diagnosis compared to those of unknown etiology, and identified a subset of patients without a monogenic etiology who were likely to have very early-onset T1D.
The T1D-GRS provides an additional tool to discriminate monogenic diabetes from T1D. Islet autoantibody testing allows some discrimination (12) but are absent in about ~10% of T1D patients at the time of diagnosis, and this proportion increases with diabetes duration (13; 14). C-peptide can be useful to discriminate between MODY and T1D but may not be helpful close to diagnosis due to the honeymoon period (15) and ~8% T1D patients have significant C-peptide (urine C-peptide creatinine ratio >0.2 nmol/mmol) secretion >5 years after diagnosis which further reduces the discriminatory power of C-peptide (16). C-peptide also cannot differentiate T1D from NDM as it is usually C-peptide negative (3). A DNA-based test such as the T1D-GRS does not change with time and can provide independent and additive information to autoantibody or C-peptide status (8).
The T1D-GRS provides a probability that a patient has T1D. Combining this test with clinical features and established biomarkers will give the greatest predictive value, for example by integrating with the MODY probability calculator (17). Importantly the T1D-GRS does not discriminate between different non-T1D subtypes of diabetes such as Type 2 diabetes and monogenic diabetes. Therefore its utility is in the group of patients where differential diagnosis for monogenic diabetes is T1D and not T2D (e.g. non-obese Caucasian patients, diagnosed <25 years).
This new tool should aid the discovery of novel genes for monogenic diabetes. The main advantage of the T1D-GRS is its ability to discriminate monogenic diabetes from T1D regardless of subtypes of monogenic diabetes. Therefore, the application of the T1D-GRS in patients in whom known monogenic subtypes have been excluded can help to identify patients with the highest probability of a novel cause of monogenic diabetes. These patients can be prioritised for exome or genome sequencing to maximise the chance of novel discoveries. A similar strategy of selecting patients for genetic testing for monogenic hypercholesterolemia using genetic risk score associated with polygenic hypercholesterolemia has been proposed (18).
We have confirmed that diabetes diagnosed under 6 months of age predominantly has monogenic etiology, with ~92% of these patients likely to have a known or novel cause of monogenic diabetes. This is consistent with previous studies that showed patients diagnosed in the first 6 months of life have the high-risk HLA alleles associated with T1D at a similar frequency to controls and rarely have autoantibodies (19; 20). Currently, all patients with neonatal diabetes diagnosed under 6 months undergo targeted-next generation sequencing for 23 genes regardless of clinical presentation (3; 11). This approach has been successful and confirmed a monogenic etiology in 82% of patients. Our data support this approach and we do not recommend excluding any patients for genetic testing based on the T1D-GRS in this age group due to the high prior probability of monogenic diabetes. We suggest that the TID-GRS be used to define which patients are likely to be T1D or monogenic NDM in a patient diagnosed before 6 months only when the known monogenic causes are excluded.
The finding of probable early-onset of T1D before 6 months of age was unexpected. Autoimmune disease in neonates is extremely rare and normally due to either transfer of maternal autoantibodies or specific monogenic defects in the immune system (21; 22). In addition, current understanding of pathophysiology of T1D suggests that autoimmune pathways are not mature enough to cause full-blown attack on the pancreas before 6-12 months of age (23; 24). T1D has a prolonged pre-clinical phase in the months to years before developing clinical T1D (25). This raises the possibility that T1D under 6 months is due to a disproportionate increase in immune-mediated destruction of beta-cells because of environmental triggers in either the pre-natal or immediate post-natal period or multiple factors such as genetics, environment and epigenetics all paying a role. Further studies of these rare patients will be important and may provide novel understanding of the biology of T1D.
Our study has limitations. We only included patients from White-European ethnicity, as this was the racial group with the most information about T1D genetic susceptibility. Further work is required to validate this method in patients from other ethnicities. We have captured the most common and high-risk alleles associated with T1D but not the recently published less frequent and lower risk alleles (4). However, these new variants will only result in minor improvement, as the major susceptibility alleles are already included in our GRS. More fine-mapping and larger studies will identify new variants and the addition of these variants can only strengthen our results due to improved capture of T1D genetic susceptibility.
In conclusion, we have shown that a T1D genetic risk score discriminates monogenic diabetes of any etiology from T1D. Using the T1D-GRS, we have identified cases of very-early onset T1D presenting under 6 months age. The T1D-GRS helps to identify patients with a potential novel monogenic etiology so they can be prioritized for exome or genome sequencing analysis.
Supplementary Material
Acknowledgements
ATH and SE are Wellcome Trust Senior Investigators and ATH is also supported by an NIHR Senior Investigator award. Additional support came from the University of Exeter and the NIHR Exeter Clinical Research Facility. KAP and RAO are supported by NIHR Clinical lecturer award. MNW is supported by the Wellcome Trust Institutional Support Fund (WT097835MF) and the Medical Research Council (MR/M005070/1). SEF has a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (105636/Z/14/Z). This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. The views expressed are those of the author(s) and not necessarily those of the Wellcome Trust, the NHS, the NIHR or the Department of Health.
Author Contributions
KAP, RAO, and MNW researched data and performed statistical analyses. KAP and ATH wrote the first draft of the manuscript, which was modified by all authors. All authors contributed to the discussion and reviewed or edited the manuscript.
Conflicts of interest
The authors have no conflicts of interest
Guarantor’s statement
Andrew Hattersley is the guarantor of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
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